Risk Prediction

HomeCirculationVol. 134, No. 19Risk Prediction Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBRisk PredictionAre We There Yet? Jesper K. Jensen, MD, PhD Jesper K. JensenJesper K. Jensen From the Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark. Originally published28 Sep 2016https://doi.org/10.1161/CIRCULATIONAHA.116.024941Circulation. 2016;134:1441–1443Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: November 8, 2016: Previous Version 1 Articles, see p 1419 and p 1430Population aging and improvements in survival after cardiovascular disease have contributed to worldwide increases in the number of patients with established cardiovascular disease. An almost epidemic number of people, >16 million Americans, suffering from ischemic heart disease serves as clear evidence of the importance of ischemic heart disease.1 Despite a decline in mortality, ischemic heart disease is now the leading cause of death worldwide, which according to estimates from the World Health Organization, will rise to 11.1 million cardiovascular deaths by 2020.2The assessment of cardiovascular disease risk and the prevention of recurrent events in patients suffering from cardiovascular disease represent an opportunity for major public health gains.3 Informal methods of risk prediction have traditionally been used to guide which individuals may benefit from therapy.4 However, due to variation in the observed and unobserved risk factors and the fact that clinicians are not good at estimating the likelihood of an outcome, risk assessment based on informal methods is not optimal.5,6 Multivariable risk models in the setting of primary prevention have been intensely studied, in contrast to patients with established cardiovascular disease, where much less data are available.4In this issue of Circulation, Kaasenbrood and colleagues7 have presented their findings regarding the 10-year risk of recurrent vascular disease events in a population with different manifestations of cardiovascular disease. The SMART risk score (Second Manifestations of Arterial disease) for 10-year risk of myocardial infarction, stroke, or vascular death was externally validated in 3 different cohorts of 18 436 patients with established cardiovascular disease (ischemic heart disease, cerebrovascular disease, peripheral artery disease, and abdominal aortic aneurysm).8 The variables included in the model were age, sex, current smoking, diabetes, blood pressure, cholesterol, coronary artery disease, cerebrovascular disease, peripheral artery disease, creatinine, and high-sensitivity C-reactive protein. Traditionally, patients with established cardiovascular disease are all considered high risk even though the data supporting this theory are sparse. Thus, the present study provides new data on how to deal with patients with established cardiovascular disease. A surprising finding in the present study is the large variation in distribution of estimated risk, with half of the patients with <10% risk of recurrent vascular events and 9% of the patients with >30% risk of recurrent events even though all guideline recommendations were reached.9 Not surprisingly, patients with polyvascular disease had the highest estimated residual risk (median 22%, interquartile range, 14% to 36%). The model presented by Kaasenbrood et al7 was fairly calibrated in all 3 external populations, but the discrimination was only modest with a c-statistic of 0.64 (95% confidence interval, 0.63–0.65) in the pooled population and approximately the same for the subgroups.Previously, there have been several other attempts to develop multivariable risk models, but discrimination and calibration were inconsistently reported.4,6,10 In addition, several previously reported studies did not validate their models in external cohorts. Thus, the methodologic approach of Kaasenbrood and colleagues is a strength and in line with a 2014 publication of 102 023 patients with stable coronary artery disease, which showed excellent discrimination and good calibration of the model used.6 In that prior study, both demographic and clinical variables were included, and the model was validated in an external cohort. The authors evaluated the clinical impact of use of the model, estimating that screening 1000 participants would lead to 15 life years saved relative to a model that only used demographic characteristics.7In their discussion, Kaasenbrood and colleagues7 note that their cohort design is useful in clinical practice and their findings generalizable to other populations with vascular disease. However, the discrimination and calibration of predictive models can vary substantially by geographic region.11 The majority of risk prediction models are developed and validated in European and Northern American populations. According to the World Health Organization, >75%of all cardiovascular deaths occur in low- and middle-income countries, but prediction models from these countries are sparse. Thus, as with any new prognostic model, further independent evaluation in different settings, including different geographic locations, is required to guide use in clinical practice.The study from Yang and colleagues12 in this issue of Circulation presents data from the China-PAR project (Prediction for Atherosclerotic Cardiovascular Disease Risk in China). The China-PAR project included a cohort of 106 281 individuals to establish a risk model for primary prevention of cardiovascular disease in a Chinese population. This model used demographic and clinical variables to predict risk of nonfatal acute myocardial infarction, cardiac death, or stroke over an average follow-up of 12.3 years. The model was externally validated in 2 cohorts, the discrimination was ≈0.80 for both men and women, and the model appeared well calibrated. The model was compared with the Pooled Cohort Equations,13 demonstrating that the Pooled Cohort Equations for white Americans and black Americans substantially overestimated risk for both Chinese men and women.In view of limited resources, finding prevention strategies and identifying high-risk persons are mandatory. Much of the current research on risk prediction has focused on novel risk factors and prognostic markers to improve risk prediction, but the performance of the models in clinical practice is largely unknown. Ideally, researchers could compare multiple models in the same external cohort to determine which ones performed the best. The 2 studies presented in this issue of Circulation did not use novel biomarkers in their models. The use of novel biomarkers, particularly natriuretic peptides and high-sensitivity cardiac troponins, has the potential to improve risk prediction in chronic coronary artery disease.14,15 However, a price to pay when including biomarkers is the possible limitation of the risk model in low- and middle-income regions, where the model may be too expensive for widespread screening. The strength of the 2 studies of Kaasenbrood et al7 and Yang et al12 is the general methodologic approach, evaluating the performance of the models in derivation and validation cohorts and estimating the clinical impact. Because the burden of cardiovascular disease is a worldwide problem, it is extremely important that risk prediction models are developed and tested in different populations. However, most models are based on a common set of predictors: age, smoking, blood pressure, and cholesterol levels. In addition to this set, several predictors have been sporadically included in models, but rarely have these models been externally validated. Thus, more emphasis is placed on repeating the process of identifying predictors and developing new models rather than validating, tailoring, and improving existing cardiovascular disease risk prediction models.In the future, risk prediction research in both primary prevention and established cardiovascular disease should consistently include evaluation and validation of current models in different settings. In addition, it is time to focus on externally validating and comparing head-to-head the most promising existing risk models, tailoring these models to local settings, investigating whether they may be extended with new predictors such as inflammation and genetic data, and quantifying the clinical impact of the most promising models. So there is definitely much work to be done, and we are not there yet, but the studies from Kaasenbrood et al7 and Yang et al12 have moved us a little step closer in risk prediction.DisclosuresNone.FootnotesCirculation is available at http://circ.ahajournals.org.The opinions in this article are not necessarily those of the editors or of the American Heart Association.Correspondence to: Jesper K. Jensen, MD, PhD, Palle Juul-Jensens Blvd 99, Department of Cardiology, Aarhus University Hospital, Aarhus N 8200, Denmark. E-mail [email protected]References1. Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES, Fox CS, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Makuc DM, Marcus GM, Marelli A, Matchar DB, Moy CS, Mozaffarian D, Mussolino ME, Nichol G, Paynter NP, Soliman EZ, Sorlie PD, Sotoodehnia N, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Executive summary: heart disease and stroke statistics–2012 update: a report from the American Heart Association.Circulation. 2012; 125:188–197. doi: 10.1161/CIR.0b013e3182456d46.LinkGoogle Scholar2. Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, Flegal K, Ford E, Furie K, Go A, Greenlund K, Haase N, Hailpern S, Ho M, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott M, Meigs J, Mozaffarian D, Nichol G, O'Donnell C, Roger V, Rosamond W, Sacco R, Sorlie P, Stafford R, Steinberger J, Thom T, Wasserthiel-Smoller S, Wong N, Wylie-Rosett J, Hong Y; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee.Circulation. 2009 Jan 27; 119(3):480–6. doi: 10.1161/CIRCULATIONAHA.108.191259. No abstract available. Erratum in: Circulation. 2009 Jan 27;119(3):e182.Google Scholar3. Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, De Simone G, Ferguson TB, Ford E, Furie K, Gillespie C, Go A, Greenlund K, Haase N, Hailpern S, Ho PM, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott MM, Meigs J, Mozaffarian D, Mussolino M, Nichol G, Roger VL, Rosamond W, Sacco R, Sorlie P, Thom T, Wasserthiel-Smoller S, Wong ND, Wylie-Rosett J. Heart disease and stroke statistics—2010 update: a report from the american heart association.Circulation. 2010; 121:e46–e215.LinkGoogle Scholar4. Morrow DA. Cardiovascular risk prediction in patients with stable and unstable coronary heart disease.Circulation. 2010; 121:2681–2691. doi: 10.1161/CIRCULATIONAHA.109.852749.LinkGoogle Scholar5. Marma AK, Berry JD, Ning H, Persell SD, Lloyd-Jones DM. Distribution of 10-year and lifetime predicted risks for cardiovascular disease in US adults: findings from the National Health and Nutrition Examination Survey 2003 to 2006.Circ Cardiovasc Qual Outcomes. 2010; 3:8–14. doi: 10.1161/CIRCOUTCOMES.109.869727.LinkGoogle Scholar6. Rapsomaniki E, Shah A, Perel P, Denaxas S, George J, Nicholas O, Udumyan R, Feder GS, Hingorani AD, Timmis A, Smeeth L, Hemingway H. Prognostic models for stable coronary artery disease based on electronic health record cohort of 102 023 patients.Eur Heart J. 2014; 35:844–852. doi: 10.1093/eurheartj/eht533.CrossrefMedlineGoogle Scholar7. Kaasenbrood L, Boekholdt SM, van der Graaf Y, Ray KK, Peters RJG, Kastelein JJP, Amarenco P, LaRosa JC, Cramer MJM, Westerink J, Kappelle LJ, de Borst GJ, Visseren FLJ. Distribution of estimated 10-year risk of recurrent vascular events and residual risk in a secondary prevention population.Circulation. 2016; 134:1419–1429. doi: 10.1161/CIRCULATIONAHA.116.021314.LinkGoogle Scholar8. Dorresteijn JA, Visseren FL, Wassink AM, Gondrie MJ, Steyerberg EW, Ridker PM, Cook NR, van der Graaf Y; SMART Study Group. Development and validation of a prediction rule for recurrent vascular events based on a cohort study of patients with arterial disease: the SMART risk score.Heart. 2013; 99:866–872. doi: 10.1136/heartjnl-2013-303640.CrossrefMedlineGoogle Scholar9. Smith SC, Allen J, Blair SN, Bonow RO, Brass LM, Fonarow GC, Grundy SM, Hiratzka L, Jones D, Krumholz HM, Mosca L, Pasternak RC, Pearson T, Pfeffer MA, Taubert KA; AHA/ACC; National Heart, Lung, and Blood Institute. AHA/ACC guidelines for secondary prevention for patients with coronary and other atherosclerotic vascular disease: 2006 update: endorsed by the National Heart, Lung, and Blood Institute.Circulation. 2006; 113:2363–2372. doi: 10.1161/CIRCULATIONAHA.106.174516.LinkGoogle Scholar10. Montalescot G, Sechtem U, Achenbach S, Andreotti F, Arden C, Budaj A, Bugiardini R, Crea F, Cuisset T, Di Mario C, Ferreira JR, Gersh BJ, Gitt AK, Hulot JS, Marx N, Opie LH, Pfisterer M, Prescott E, Ruschitzka F, Sabate M, Senior R, Taggart DP, van der Wall EE, Vrints CJ, Zamorano JL, Baumgartner H, Bax JJ, Bueno H, Dean V, Deaton C, Erol C, Fagard R, Ferrari R, Hasdai D, Hoes AW, Kirchhof P, Knuuti J, Kolh P, Lancellotti P, Linhart A, Nihoyannopoulos P, Piepoli MF, Ponikowski P, Sirnes PA, Tamargo JL, Tendera M, Torbicki A, Wijns W, Windecker S, Valgimigli M, Claeys MJ, Donner-Banzhoff N, Frank H, Funck-Brentano C, Gaemperli O, Gonzalez-Juanatey JR, Hamilos M, Husted S, James SK, Kervinen K, Kristensen SD, Maggioni AP, Pries AR, Romeo F, Ryden L, Simoons ML, Steg PG, Timmis A, Yildirir A. 2013 ESC guidelines on the management of stable coronary artery disease: The task force on the management of stable coronary artery disease of the european society of cardiology.Eur Heart J. 2013; 34:2949–3003.CrossrefMedlineGoogle Scholar11. Moons KG, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, Altman DG, Woodward M. Risk prediction models: II. External validation, model updating, and impact assessment.Heart. 2012; 98:691–698. doi: 10.1136/heartjnl-2011-301247.CrossrefMedlineGoogle Scholar12. Yang X, Li J, Hu D, Chen J, Li Y, Huang J, Liu X, Liu F, Cao J, Shen C, Yu L, Lu F, Wu X, Zhao L, Wu X, Gu D. Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: the China-PAR project (Prediction for ASCVD Risk in China).Circulation. 2016; 134:1430–1440. doi: 10.1161/CIRCULATIONAHA.116.022367.LinkGoogle Scholar13. Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O'Donnell CJ, Robinson JG, Schwartz JS, Shero ST, Smith SC, Sorlie P, Stone NJ, Wilson PW, Jordan HS, Nevo L, Wnek J, Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG, Curtis LH, DeMets D, Hochman JS, Kovacs RJ, Ohman EM, Pressler SJ, Sellke FW, Shen WK, Smith SC, Tomaselli GF; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.Circulation. 2014; 129(25 Suppl 2):S49–S73. doi: 10.1161/01.cir.0000437741.48606.98.LinkGoogle Scholar14. Di Angelantonio E, Chowdhury R, Sarwar N, Ray KK, Gobin R, Saleheen D, Thompson A, Gudnason V, Sattar N, Danesh J. B-type natriuretic peptides and cardiovascular risk: systematic review and meta-analysis of 40 prospective studies.Circulation. 2009; 120:2177–2187. doi: 10.1161/CIRCULATIONAHA.109.884866.LinkGoogle Scholar15. Omland T, Pfeffer MA, Solomon SD, de Lemos JA, Røsjø H, Šaltytė Benth J, Maggioni A, Domanski MJ, Rouleau JL, Sabatine MS, Braunwald E; PEACE Investigators. Prognostic value of cardiac troponin I measured with a highly sensitive assay in patients with stable coronary artery disease.J Am Coll Cardiol. 2013; 61:1240–1249. doi: 10.1016/j.jacc.2012.12.026.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By McKay A, Gunn L, Ference B, Dorresteijn J, Berkelmans G, Visseren F and Ray K (2021) Is the SMART risk prediction model ready for real-world implementation? A validation study in a routine care setting of approximately 380 000 individuals, European Journal of Preventive Cardiology, 10.1093/eurjpc/zwab093, 29:4, (654-663), Online publication date: 30-Mar-2022. Nurmohamed N, Belo Pereira J, Hoogeveen R, Kroon J, Kraaijenhof J, Waissi F, Timmerman N, Bom M, Hoefer I, Knaapen P, Catapano A, Koenig W, de Kleijn D, Visseren F, Levin E and Stroes E (2022) Targeted proteomics improves cardiovascular risk prediction in secondary prevention, European Heart Journal, 10.1093/eurheartj/ehac055, 43:16, (1569-1577), Online publication date: 21-Apr-2022. Liu N, Liang G, Li L, Zhou H, Zhang L and Song X (2021) An eyelid parameters auto-measuring method based on 3D scanning, Displays, 10.1016/j.displa.2021.102063, 69, (102063), Online publication date: 1-Sep-2021. Zullig L, Egbuonu-Davis L, Trasy A, Oshotse C, Goldstein K and Bosworth H (2018) Countering clinical inertia in lipid management: Expert workshop summary, American Heart Journal, 10.1016/j.ahj.2018.09.003, 206, (24-29), Online publication date: 1-Dec-2018. November 8, 2016Vol 134, Issue 19 Advertisement Article InformationMetrics © 2016 American Heart Association, Inc.https://doi.org/10.1161/CIRCULATIONAHA.116.024941PMID: 27682884 Originally publishedSeptember 28, 2016 KeywordspreventionEditorialsrisk scorerisk predictionrisk stratificationPDF download Advertisement SubjectsCardiovascular DiseasePrimary PreventionSecondary Prevention